Results are logged in tab:rezendekl. Notably, our model
significantly outperforms both baseline models with a KL of \(0.003\) ,
almost an order of magnitude smaller than the runner-up with a KL of
\(0.05\) . This highlights the expressive power of the RCPM model
class. We also provide a visualization of the trained RCPM in
fig:sphere-data (top row), where we show KDE estimates performed
in spherical coordinates with a bandwidth of 0.2. Finally,
tab:rezenderuntime compares the runtime per training iteration of our
model with [1]}’s models over 1000 trials with
a batch size of 256. Our model’s speed is comparable to
theirs while leading to significantly improved KL/ESS.
